Chapter 5 Relationship of Economic Value Added and Conventional Performance Measures ...shodhganga.inflibnet.ac.in/bitstream/10603/5127/10/10... · 2015-12-04 · Chapter 5 Relationship

Chapter 5

Relationship of Economic Value Added and Conventional

Performance Measures with Market Value Added

5.1 Introduction

Although Shareholder Value Creation has become the widely accepted corporate mission,

much debate is taking place at its measurement level. As much as companies intensify to

fulfill their vision of creating value for their shareholders, the quite obvious question

arises i.e. which measurement metric is best among all. Lay investors and even most

companies tend to focus too much on size and income based metrics such as share price

(market value or market capitalization), earnings, growth in earnings, EPS, ROCE and

ROE. But such metrics don‟t consider the cost of equity capital and are influenced by

accrual accounting based conventions. Moreover, these traditional accounting measures

do not take into account how much capital has been poured into the business to generate

the additional income, so it is relatively easy to improve such measures simply by

investing more. Thus, there are so many reasons (refer shortcomings of traditional

financial performance measures discussed in Chapter 1) due to which these traditional

measures have been regularly criticized as misleading, manipulative and incompetent in

disclosing an organization‟s value creating performance.

In turn, proponents of value based measures have responded with specific metrics and

methodologies that claim to provide a better and reliable measurement of shareholder

value creation (Armitage, 1995). For instance, US-based Consultancy firm Stern-Stewart

& Company claimed that „earnings, earning per share and earnings growth are misleading

measures of corporate performance and the best practical periodic performance measure

is Economic Value Added (EVA)‟. Stewart (1991) argued that EVA comes closer than

any other measure to capture the true economic profitability of an enterprise and is the

performance measure, which is most directly linked to the shareholder value over time.

To further support his claim, Stewart (1994) provided empirical evidence that „EVA

stands well out from the crowd as the single best measure of wealth creation on a

contemporaneous basis and is almost 50% better than its closest accounting based

competitors (including EPS, ROE and ROI) in explaining changes in shareholder wealth‟.

Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added

147

The proponents also claimed that mathematically, the EVA of a company is the net

present value (NPV) of all its future EVAs. Thus, a company that continues to improve

economic value added, year after year, will sooner than later, find favor with investors.

Thus, over the long term, it is an improvement in EVA and not in accounting results that

derives wealth creation. That is one reason why companies world over need to focus on

improving their fundamental economic performance as measured by EVA.

The literature for the relationship between EVA and Market Value involves a

considerable debate regarding the superiority of EVA in comparison to the traditional

performance measures like ROI, EPS, ROCE etc. This chapter of the dissertation is

devoted to identify the result of this metric war (between traditional and value based

measures of performance) empirically. It attempts to investigate “Does EVA dominate

Earnings in Indian corporate sector?” Thus, the present study will be of immense use to

financial analysts, corporate officials, researchers and policy makers who may be

interested in EVA as replacement (or compliments) to earnings as key measure of

corporate performance.

Worth to be mentioned here that in a letter to the editor of Management Accounting,

Stewart criticized studies that evaluated EVA‟s effectiveness in estimating value added

by measuring how it explains stock returns, calling them “meaningless and unimportant

for the purposes of validating EVA. Stewart argued that using EVA as a proxy for MVA

is what carries more importance. Thus, the present study considers MVA as a proxy for

shareholder wealth created or eroded by the sample companies.

5.2 Sample Description and Database

Initially, sample size of the study remained same i.e. 104 Companies (as described in

section 3.2 of Chapter 3). However, while examining data, four companies namely

Satyam Computer Services Ltd., Tata Steel Ltd., Sun Pharmaceutical Industries Ltd.,

Sesa Goa Ltd. and were identified as outliers and had to be deleted. Thus, a final sample

of 100 companies was selected and studied for the subsequent analysis. Secondary data

has been used for a period of 12 years i.e. from 1996 to 2007. All the relevant financial

information has been sourced from the CMIE‟s corporate database Prowess and the data

regarding share prices has been obtained from the Capitacharts of Capital Market


148

Publishers of India Ltd. E-Views and Statistical Package for Social Sciences (SPSS) have

been used for the analysis of data.

5.3 Hypothesis of the Study

Relative information content comparisons are appropriate when one desires a ranking of

performance measures by information content or when making mutually exclusive

choices among performance measures i.e. when only one measure can be chosen. In

contrast, Incremental information content comparisons assess whether one measure

provides value-relevant inferences beyond those provided by another measure, evaluating

the benefit of supplemental disclosures in financial reporting (Biddle et al., 1997). As the

literature contains both i.e. studies favorable to EVA as well as those which disagree with

EVA to be the best predictor of MVA, the present study takes a neutral position. It tests

the hypothesis that Value Based Measures as well as Traditional Financial Performance

Measures have equal relative and incremental information content i.e. equal association

with MVA, a surrogate of shareholder value creation.

5.4 Choice of Variables

Nine independent financial variables are chosen for the purpose of the study, of which

five represent Accrual Accounting based traditional performance measures, two are

Value Based Performance Measures and the remaining two are Economic Variables.

Accrual accounting based performance measures includes Return on Capital Employed

(ROCE), Return on Net Worth (RONW), Profit after Tax (PAT), Earning per Share

(EPS) and Return on Total Assets (ROTA) whereas Value based measures include

Economic Value Added (EVA) and EVA in percentage terms (EVA%). Employees‟

Productivity (Ep) and Capital Productivity (Cp) are the performance measures

representing Economic Indicators. For testing the hypothesis, Market Value Added

(MVA) has been taken as the dependent variable. A brief description of all these

variables is given in Table 5.1.

5.4.1 Dependent Variable

Market Value Added (MVA): MVA being an absolute measure assesses that how much

capital a company has added to or subtracted from its shareholders‟ investment. It is the

cumulative amount by which a company is perceived to have enhanced or diminished

shareholder wealth. It is based upon the logic that if the total market value of a company


149

is more than the capital invested in it, the company has managed to create shareholder

value. However, if the market value of a company comes less than its invested capital,

company has destroyed the shareholder value. MVA thus, measures the value added by

the management over and above the capital invested in the company by its shareholders

and lenders. For the purpose of the study, MVA is obtained by subtracting the economic

capital of a corporation (book value after adjusting for economic anomalies) from its total

market value i.e. what investors can take out of the company.

Mathematically,

MVA = Market Value of the firm – Economic Capital

Hence, the way in which shareholder wealth is maximized is by increasing the difference

between the company‟s market value and its economic capital. Market value of a firm as

represented by market value of its equity is arrived at by multiplying the stock price by

the number of outstanding shares of the firm. Taking share price at the end of the

financial year for the calculation of the market capitalization can be biased. Hence, in the

present study, 364-days average market cap has been taken as proxy for the market value

of equity. Market value of the firm has been taken as the sum of book value of debt and

364-days average market capitalization.

MVA is the perfect summary assessment of corporate performance that shows how

successful a company has been in allocating and managing resources to maximize the

value of the enterprise and the wealth of its shareholders (Stewart, 1994). In the present

study, MVA being the surrogate of shareholder wealth addition has been taken as the

dependent variable.

5.4.2 Explanatory Variables

i. Return on capital employed (ROCE)

ROCE measures the profit which a firm earns on investing a unit of capital and tells

whether the company‟s borrowing policy was wise economically and whether the capital

had been employed fruitfully (Maheshwari, 2004). If the long-term return of a business

enterprise is not satisfactory in any case, then the deficiency needs to be corrected and the

activity can be abandoned for a more favourable one (Kishore, 2002).

Obviously, it is quite impractical to assess profits or profit growth properly without

relating them to the amount of funds (capital) that were employed in making profits.


150

ROCE is one of the most important profitability ratios which assess how much the capital

invested has earned during the period. It is determined by dividing net profit or income

by the capital employed or investment made to achieve that profit. The ROCE is

determined by the formula:

Adjusted Net Profit

ROCE = × 100

Capital Employed

Higher a company‟s ROCE, stronger will be its financial position. ROCE has two

components – profit as a percentage of sales (Profit margin) and sales as a percentage of

capital employed (Investment turnover). Alternatively, it can be defined as:

PAT Sales

× × 100 Sales Average Capital Employed

A firm can improve its ROCE by increasing one or both of its components viz., profit

margin i.e. (PAT / Sales) and the investment turnover i.e. productivity of its capital

employed (Sales / ACE). It also indicates that a company with higher operating profit

margin may have a lower ROCE if its asset efficiency is poor. Thus ROCE analysis

provides a strong incentive for optimal utilization of the assets of the company and is

used as a measure of success of a business. For the purpose of the study, it is expected

that ROCE will not only find a significant reflection in the market value addition of a

company but will also be a significant predictor of the same.

ii. Return on Net Worth (RONW)

This ratio measures the relationship between net profits and proprietor‟s funds and thus,

reveals how well the firm has used the resources of owners. So, this ratio is of great

interest to the present as well as prospective shareholders and also of great concern to

management, which has the responsibility of maximizing the owners‟ welfare. Further,

RONW is also capable to reveal the relative performance and strength of the company in

attracting future investments. It is calculated by the formula:

Net profit after interest and taxes

RONW =

Net worth


151

Where,

Net worth = Equity Capital + Reserves and surplus + Preference share capital –

Accumulated losses

It is believed that the stock prices and hence the market capitalization reacts favorably to

an improvement in RONW (Misra and Kanwal, 2004). This expected positive

relationship leads to improvement in the market value added of a company. Thus, RONW

is selected as one of the independent variable having positive relationship with MVA of a

company. This variable is also a relative measure and has been expressed in percentage

terms.

iii. Profit after Taxes (PAT)

It is the net profit earned by the company after deducting all expenses like interest,

depreciation and tax. It is defined as:

PAT = EBDIT –Depreciation – Interest – Taxes

Where, EBDIT is earnings before depreciation, interest and taxes.

PAT is expressed in absolute (Rupee) terms. It has been selected as an independent

variable as normally it is expected to have a positive correlation with the MVA i.e.

increasing Profitability in a well functioning capital market is likely to give a boost to the

share prices, market capitalization and market value added. A company, whose

profitability is not sufficient to cover up its overall cost of capital, face adverse EVA

situation, the result of which is the decline in its stock prices and therefore, its Market

Value also falls. For the purpose of the study, PAT figures are taken from the corporate

database, Prowess.

iv. Earning per share (EPS)

EPS is an absolute measure of profitability that identifies how much each share has

earned for the shareholders. Investors, in general, look upon earnings per share as the best

yardstick to analyze their investment decisions. It is calculated by the formula:

Net profit after tax – Preference dividend

EPS =

Total number of Outstanding Equity shares

Traditionally it is believed that EPS has a positive relationship with share prices and

hence MVA (Misra and Kanwal, 2004). It is also considered as one of the major factors


152

affecting the dividend policy of the firm and market prices of the company (Kishore,

2002). Thus, the study expects a positive association between EPS and MVA of a

company and EPS has been taken as an independent variable affecting market value

addition of a company.

v. Return on Total Assets (ROTA)

The ROTA measures a company's profits before interest and taxes (PBIT) against its total

assets. It is considered as an indicator of how effectively a company is using its assets to

generate earnings before the contractual obligations must be paid. This ratio takes out the

impact of interest and tax to depict clearly how well the operational managers have done

with the assets of the company. ROTA is calculated as:

Profit before Interest and taxes

ROTA = × 100

Total Assets

Greater a company's earnings in proportion to its assets, more efficient that company is

considered in using its assets and contributing towards firm value and shareholders‟

wealth.

vi. Economic Value Added (EVA)

EVA is conceptually a superior measure of performance because it charges management

for using capital at an appropriate risk-adjusted rate, and it eliminates financial and

accounting distortions to the extent it is practical to do so (Stewart, 1994). Economic

Value Added is calculated as the difference between NOPAT and the stakeholders‟

expectations, which is the capital charge for both debt and equity i.e. overall cost of

capital. Operationally defined,

EVA = NOPAT – Capital charge

= NOPAT – WACC × Economic Capital

Where, NOPAT is Net Operating Profits after adjusting for non-operating items, non-

recurring events and other economic adjustments to compute economic profits from

accounting profits. The detailed explanation of these adjustments is given in Chapter 4.

NOPAT = (PAT + non-recurring expenses + revenue expenditure on R & D + interest

expense + goodwill written off + provision for taxes) - non-recurring income

- R & D amortization – cash operating taxes.


153

WACC = Weighted average cost of capital.

= Cost of equity × proportion of equity in total capital + Cost of debt ×

proportion of debt in total capital (1 – tax rate) + Cost of preference capital ×

proportion of preference capital in total capital.

Economic Capital = Net Fixed Assets + Investments + Current Assets – (NIBCLs +

Miscellaneous Expenditure not written-off + Intangible Assets) + (Cumulative

Non-Recurring Losses + Capitalized expenditure on R & D + Gross

Goodwill) – Revaluation Reserve – Cumulative Non-Recurring Gains

Grant (2003) emphasized that EVA is the internal performance measure that is most

highly correlated with MVA and provides the most reliable guide to- whether and by how

much, management actions have contributed to shareholder wealth. Grant (2003) also

stated that companies having positive EVA momentum should on balance see their stock

prices go up over times as the increasing profits, net of the overall capital costs lead to a

rise in the company‟s Market Value Added. Moreover, the relationship between MVA

and EVA has also been supported empirically by a number of prominent researchers

(section 2.1 in chapter 2). The purpose of selecting EVA as an important explanatory

variable is to identify in Indian context that to what extent EVA is associated with MVA

and whether EVA dominates other traditional performance measures in explaining the

changes in MVA of sampled companies.

vii. EVA as a percentage of capital employed (EVACE)

It indicates that how much value has been added by the company at given level of capital

employed and is determined by the formula:

EVA

EVA as a % of capital employed = × 100

Invested capital

The logic behind considering this relative measure of value creation as independent

variable is to identify that what explains MVA more; EVA in absolute terms or EVA as a

relative measure. This ratio can assist the policy makers to infer whether the market‟s

response to the relative measures of financial performance is better than that to the

absolute measures of financial performance (Misra and Kanwal, 2004).


154

viii. Capital Productivity (Cp)

Capital productivity captures revenue generated per unit of capital employed and

indicates the productivity taken out of the fixed assets of a company. This ratio improves

when a company manages to generate more revenue out of the same assets i.e. through

better utilization of its capital resources. It is calculated as:

(Net Sales + Change in Stock – Raw Material Consumed – Power and Fuel Cost)

Cp =

Net Fixed Assets

Where, Net Sales is Gross Sales net of indirect taxes and Net Fixed Assets are Gross

block net of accumulated depreciation. It is believed that shareholders value can improve

only when capital productivity improves. If fixed assets are efficiently used, it would

generate wealth for the stakeholders in a company and more particularly for equity

shareholders (Misra and Kanwal, 2004). Thus, Cp is expected to be a significant predictor

of market value added of a company.

ix. Employees’ Productivity (Ep)

Employees‟ productivity is the ratio of (the real value of) output to the input of

employees. This is defined as:

(Net Sales + Change in Stock – Raw Material Consumed – Power and Fuel Cost)

Ep =

Salaries & Wages

To calculate Ep, denominator i.e. employees‟ input can be expressed in terms of hours

worked, numbers of employees or expenses on salaries and wages. Based upon Review

of literature (Banerjee and Jain, 1999; Bhatnagar et al., 2004; Misra and Kanwal, 2004

and Singh and Garg, 2004), this study uses salaries and wages expenditure as a surrogate

of employees‟ input. Thus, Ep denotes value addition per rupee of salaries and wages bill.

Ep will improve if value addition improves for the same level of salaries and wages i.e. if

the efficiency of workforce improves and/or if same value can be achieved with lower

salaries and wages cost, which implies that the less number of employees can do the same

job with equal efficiency (Misra and Kanwal, 2004). It is expected that the employees‟

productivity significantly affect the profitability of a company and consequently

establishes a significant relationship with shareholder value (MVA) of a company.


155

Table 5.1: Variables used in the Data Set and Other Formulas Applied

Variables Name Notations Description

1. Dependent Variable

Market Value Added MVA Market Capitalization-Economic Capital

2. Independent Variables

Economic Value Added EVA NOPAT-( WACC *Economic Capital)

Economic Value Added (%) EVA% (EVA/Economic Capital) * 100

Earning Per Share EPS (PAT-Dividend on Pref. Shares)/No. of outstanding Equity

Shares

Return on Capital

Employed

ROCE (PAT nnrt/Average Capital Employed)*100

Return on Average Net

Worth

RONW (PAT/Average Net Worth)*100

Return on Total Assets ROTA (Profit before Interest and Taxes/Total Assets) x 100

Profit After Taxes PAT The profit earned by the company after accounting for all

expenditures (operational, selling & distribution,

administrative & other overheads and financial costs) is

included under this datafield.

Capital Productivity Cp (Net Sales + Change in Stock – Raw material consumed –

Power and Fuel Costs) / Net Fixed Assets

Employees Productivity Ep (Net Sales + Change in Stock – Raw Material Consumed –

Power and Fuel Cost)/Salaries & Wages

3. Other Formulas Applied

Market Capitalization Market

Cap

365 days Avg. Market Cap. + Average Borrowings

Economic Capital EC Net Fixed Assets + Investments + Current Assets –

(NIBCLs + Miscellaneous Expenditure not written-off +

Intangible Assets) + (Cumulative Non-Recurring Losses +

Capitalized expenditure on R & D + Gross Goodwill) –

Revaluation Reserve – Cumulative Non-Recurring Gains

Net Operating Profit After

Taxes

NOPAT (PAT + non-recurring expenses + revenue expenditure on R

& D + interest expense + goodwill written off + provision

for taxes) - non-recurring income - R & D amortization –

cash operating taxes

Weighted Average Cost of

Capital

WACC

Ke = cost of equity shareholders‟ funds

Kd = cost of debt

Kp = cost of preference capital

E = book value proportion of average shareholders‟ funds

D = book value proportion of average total borrowings

P = book value proportion of average preference capital.


156

5.5 Statistical Diagnostic

Initially, the study used Partial Regression Plots as the detection method to identify

observations that were outliers on the dependent variables. The analysis indicated four

companies namely Satyam Computer Services Ltd., Tata Steel Ltd., Sun Pharmaceuticals

Industries Ltd. and Sesa Goa Ltd. to be unrepresentative involving extreme values. Thus,

these four companies had to be eliminated from the further analysis.

Before proceeding to the further analysis, the existence of multicollinearity among

independent variables had also been taken care of. For this purpose, Pearson‟s correlation

matrix was at first formed that signaled high correlation among various independent

variables i.e. ROCE, RONW, ROTA, and EVA%, causing the problem of

multicollinearity. To overcome this problem, various combinations of the independent

variables were created and tested. Finally on the basis of „Best Model Fit Criteria‟, highly

collinear variables i.e. RONW, ROTA, and EVA % were omitted from the model.

Further, two variables i.e. Ep and EPS were also eliminated from the model due to their

negligible and insignificant correlation with the dependent variable.

Table 5.2: Correlation Co-efficient Matrix with Selected Variables (1996-2007)

Variables EVA ROCE PAT CP MVA

EVA 1.000 .526* .357* .162* .520*

ROCE .526* 1.000 .213* .265* .392*

PAT .357* .213* 1.000 -.033 .724*

CP .162* .265* -.033 1.000 .083*

MVA .520* .392* .724* .083* 1.000

* Correlation is significant at the 0.01 level (2-tailed).

Table 5.2 provides the Correlation Matrix for MVA and the four selected independent

variables for the period 1996-2007. Correlation is an extremely useful tool to estimate the

strength of the relationship between the corresponding pair of variables in a correlation

matrix. The analysis of the table reveals that all the selected variables are positively and

significantly associated with MVA. The highest positive relationship exists between PAT

and MVA at .724. That means, as PAT increases, there would be an increase in the

shareholder value. The similar positive relationship can also be observed between EVA


157

and MVA as well as between ROCE and MVA. As far as correlation among independent

variables is concerned, the maximum correlation can be observed between EVA and

ROCE at .526 which is much lesser than the prescribed rule of thumb of 0.8 (Gujarati,

1995). Hence, it can be claimed that multicollinearity does no longer exist in the selected

regression model. In addition, the study also undertakes to consider Average Variance

Inflating Factor (VIF) to detect multicollinearity. Durbin-Watson statistics has been

employed to check the assumption of independent errors (auto-correlation). The White

Procedure is applied to ensure that coefficients are not heteroscedastic.

5.6 Model Development

The next methodological requirement is to specify the regression model used to compare

the relative information content of the competing measures of firm performance (Value

Based Measures as well as Traditional Financial Performance Measures) on the basis of

their association with MVA. The following model has been selected for the purpose of

Panel Data Analysis i.e.

MVAit = α + β1 PATit + β2 ROCEit + β3 Cpit + β4 EVAit + eit ……………. Equation 5.1

The dependent variable in the above equation is the Market Value Added (MVA) for firm

i in period t. The explanatory variables in the model are Profits after Taxes (PAT), Return

on Capital Employed (ROCE), Capital Productivity (Cp) and Economic Value Added

(EVA). Following the literature on the relative information content of various firm

performance measures, the hypothesis suggests positive coefficients for PAT, ROCE, Cp

and EVA when specified as explanatory variables for MVA. It also suggests that the

more closely these measures approximate market value addition, the higher will be the

relative information content of these measures. This model is estimated using a pooled

time-series, cross-sectional least squares regression.

Test for Relative and Incremental Information Content

To assess relative and incremental information content, the study employs a statistical

test from Biddle et al. (1997) that allows a test of the null hypothesis of no difference in

the ability of two competing sets of independent variables to explain variation in the

dependent variable. Using this test, the study makes four univariate regressions (between

MVA and each of the four independent variables) and six pairwise comparisons of

regressions among the value based and accounting performance measures namely EVA,

PAT, ROCE and Cp. The test is constructed as a comparison of R2s (Biddle et al., 1997).


158

5.7 Empirical Results and Discussions

To select the most appropriate pooling technique, the study estimated coefficients,

standard errors and t-statistics for the model across three alternative pooling techniques

i.e. pooled results, fixed effects and random effects. In the first instance, a pooled

regression was run the results of which are presented in table 5.3. These results reported

an adjusted R2 of .618 and F statistic was also found to be significant (p<.001).

Table 5.3: Multivariate Results of Pooled Regression Analysis (Restricted Model)

Variable Coefficient Std. Error t-Statistic Prob.

C -509.1082 249.3568 -2.041685 0.0414

EVA 6.756939 0.663294 10.18695 0.0000

PAT 7.617715 0.238220 31.97770 0.0000

ROCE 81.56281 13.07449 6.238315 0.0000

CP 64.34786 38.93651 1.652636 0.0987

R-squared 0.617976

Adjusted R-squared 0.616697

F-statistic 483.2684

Prob (F-statistic) 0.000000

However, the pooled regression does not anticipate the firm or time specific effects. To

consider whether firm-specific or time-specific factors had any significant effect on the

dependent variable-MVA, there was a need to estimate both fixed and random effect

models with firm, time or both effects. Thus, at first, Fixed Effects were observed for

cross sections, the results of which are presented in Table 5.4.

Table 5.4: Multivariate Regression Results with Cross-Sectional Fixed Effects and

No Period Effects


C 296.6956 241.3386 1.229375 0.2192

EVA 2.467593 0.714615 3.453038 0.0006

PAT 8.853163 0.302517 29.26503 0.0000

ROCE 15.73601 13.32593 1.180856 0.2379

CP 33.00524 42.73093 0.772397 0.4400

Effects Specification

Cross-section fixed (dummy variables)

R-squared 0.821695





159

To test the joint significance of firm effects in the fixed effects model, the Redundant

Fixed Effects-Likelihood Ratio was obtained.

Table 5.5: Results of the Cross-Sectional Redundant Fixed Effects-Likelihood Ratio

Effects Test Statistic d.f. Prob.

Cross-section F

Cross-section Chi-square

12.648627

914.385465

(99,1096)

99

0.0000

0.0000

Table 5.5 presents the results of the Cross-Section Redundant Fixed Effects that provided

F-statistic being significant at 1% level. It indicated that the Fixed Effects Regression

Model (Least Squares Dummy Variable Regression Model) was valid i.e. the fixed

effects were found to be efficient among cross-sections in the sample. Similarly Fixed

Effects were examined for time period, the results of which are provided in tables 5.6 and

5.7. In this case also, fixed effects were found to be efficient for the study period.

Table 5.6: Multivariate Regression Results with Period Fixed Effects and No Cross-

Sectional Effects


C -496.3919 253.0658 -1.961513 0.0501

EVA 6.704621 0.662044 10.12716 0.0000

PAT 7.544267 0.241169 31.28208 0.0000

ROCE 81.22347 13.22883 6.139883 0.0000

CP 67.00540 38.90336 1.722355 0.0853


Period fixed (dummy variables)

R-squared 0.625922




Table 5.7: Results of the Period Redundant Fixed Effects-Likelihood Ratio

Effects Test Statistic d.f. Prob.

Period F

Period Chi-square

2.286455

25.223919

(11,1184)

11

0.0092

0.0085


160

Further, the study attempted to choose between fixed and random effects specification.

This was accomplished applying the Hausman test in each case. As explained earlier, the

Hausman test is a test of H0: that random effects would be consistent and efficient, versus

H1: that random effects would be inconsistent. (Here, fixed effects would certainly be

consistent.). As for the fixed effects, the random effects could also be along either the

cross sectional or period dimensions. Thus, at first the random effects were selected for

the firms (i.e. cross-sectional) and not over time. Here, the slope coefficients were quite

different compared with both pooled and fixed effects regressions. Thus, there was a need

to identify whether the random effects model passed the Hausman test for the random

effects being uncorrelated with the explanatory variables. The results of the Hausman test

based on the firm random effects indicated the p-value for the test being less than 1%. It

indicated that the random effects model was not appropriate and fixed effects

specification was to be preferred in cross-sectional analysis. Table 5.8 reports the top

panel of the Hausman test results for firms.

Table 5.8: Results of the Cross-Section Random Effects - Hausman Test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 34.685493 4 0.0000

Following the similar procedure, next, the random effects were selected for the period

dimension. Here, as presented in table 5.9, the results of the Hausman test indicated an

insignificant p-value of .6488. It indicated the acceptance of null hypothesis that random

effects were efficient and consistent as far as period dimension was concerned.

Table 5.9: Results of the Period Random Effects - Hausman Test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Period random 2.476603 4 0.6488

Thus, finally the model selected was fixed effects for firms (cross-sectional) dimension

and random effects for period dimension.


161

Table 5.10: Results of the Multivariate Panel Data Regression Analysis (Final Model

with Cross-Sectional Fixed Effects and Period Random Effects)


C 576.0233 249.9763 2.304311 0.0214

EVA 2.607736 0.702984 3.709523 0.0002

PAT 8.503470 0.306100 27.78004 0.0000

ROCE 2.783971 13.60174 0.204678 0.8379

CP 27.00575 42.15328 0.640656 0.5219


S.D. Rho

Cross-section fixed (dummy variables)

Period random 718.8874 0.0424

Idiosyncratic random 3415.066 0.9576

Weighted Statistics

R-squared 0.819675



Prob(F-statistic) 0.000000

Unweighted Statistics

R-squared 0.821113

Table 5.10 provides that the variability in the MVA accounted for by the four final

predictors comes out to be 81.97% (R2). A high and positive value of Adjusted R

2 at

80.27% verifies that the cross- validity of this model is very good. F-statistic is found to

be large (48.368) and significant (at 1% level). The results also show that all the selected

independent variables i.e. EVA, PAT, ROCE and Cp have positive slope coefficients (i.e.

β values) showing their positive association with MVA. However, tested on the basis of

t-statistic, just two independent variables i.e. EVA and PAT are identified as the

significant predictors of MVA at 1% confidence level (p<.001). On the other hand ROCE

and Capital Productivity i.e. Cp do not seem to have established a significant statistical

association with MVA. As explained earlier, multicollinearity is also not a cause of

concern in the model and has properly been accounted for. Thus, the above indicators

claim the regression model to be statistically fit and valid.


162

Table 5.11: Summary of the Results of the Association of Independent Variables with Market Value Added

Models

EVA PAT ROCE Cp

F Adj. R2 Estimated

Coefficient

Standard

Error

t-

statistics

Estimated

Coefficient

Standard

Error

t-

statistics

Estimated

Coefficient

Standard

Error

t-

statistics

Estimated

Coefficient

Standard

Error

t-

statistics

I 13.148 .674 19.50

(.000)

53.719

(.000) 0.3454

II 9.066 .243 37.258

(.000)

152.44

(.000) 0.6025

III 80.587 18.472 4.363

(.000)

16.805

(.000) 0.5940

IV 36.301 59.314 .6120

(.5406) 16.756 (.000)

0.1362

V 2.708 0.651 4.157 (.000)

8.509 0.304 28.009 (.000)

49.413 (.000)

0.8031

VI 9.169 0.252 36.430

(.000) 23.780 12.469

1.907

(.0568)

48.682

(.000) 0.8007

VII 13.591 0.749 18.145

(.000) -40.452 17.510

-2.310

(.021)

24.620

(.000) 0.6881

VIII 9.070 0.243 37.324

(.000) 60.654 40.229

1.508

(.132)

140.837

(.000) 0.6026

IX 81.991 18.763 4.370

(.000) -26.947 62.445

-0.432

(.666)

16.645

(.000) 0.5937

X 13.181 0.676 19.491 (.000)

-36.727 51.775 -0.709 (.478)

49.620 (.000)

0.3452

XI 2.608 0.703 3.710

(.000) 8.503 0.306

27.780

(.000) 2.784 13.602

0.205

(.838) 27.006 42.153

0.641

(.522)

48.368

(.000) 0.8027

Note: The dependent variable is Market Value Added (MVA) and explanatory variables represent one value based measure i.e. Economic Value Added (EVA), and

three accounting based measures namely Profit after Taxes (PAT), Return on Capital Employed (ROCE) and Capital Productivity (Cp). The first four models (I to

IV) present the results of the univariate association of each independent variable with the dependent variable- MVA. In the next six models (V to X), independent

variables are specified in pair-wise combinations and finally considered jointly in the last model (XI).


163

Relative and Incremental Information Content Tests

Table 5.11 presents the estimated co-efficient, standard errors, t-statistics, F-statistic and

adjusted R2 for each model. The dependent variable is Market Value Added (MVA) and

explanatory variables represent one value based measure i.e. Economic Value Added

(EVA), and three accounting based measures namely Profit after Taxes (PAT), Return

on Capital Employed (ROCE) and Capital Productivity (Cp). The first four models (I to

IV) present the results of the univariate association of each independent variable with the

dependent variable- MVA. In the next six models (V to X), independent variables are

specified in pair-wise combinations and finally considered jointly in the last model (XI).

The detailed regression results for each of univariate and bivariate (pair-wise) models are

given in table 1 through table 10 of Appendix B.

Table 5.11 clearly shows that PAT is the most significant predictor of MVA when it is

considered univariately as well as when paired with EVA. Similarly, ROCE is also found

to be significant by itself and when compared with PAT. The pair-wise regressions that

best explain the variations in MVA are EVA/PAT (80.31%), PAT/ROCE (80.07%),

EVA/ROCE (68.81%), PAT/Cp (60.26%), ROCE/Cp (59.37%) and EVA/Cp (34.52%).

Here, EVA comes twice among the best three pair-wise regressions which evidence EVA

to be a highly significant explanatory variable. However, profit after taxes (PAT) can

clearly be observed as the best predictor of MVA and is thus, recognized as the most

legitimate and reliable measure of shareholder value creation. Further PAT is followed by

ROCE, which depicts a slightly less explanatory power of 59.40% in comparison to

60.25% for PAT. These results show that traditional measures of performance have

emerged as the more dominating determinants of MVA during the study period.

Table 5.12 presents the summary results of regressions based on the Relative and

Incremental Information Content tests. Panel A of the table summarizes the significant

differences in the relative information content between accounting and value based

measures. The results of single coefficient regressions clearly show that R2 (PAT)> R

2

(ROCE)> R2 (EVA)> R

2 (Cp) where R

2 depicts the percentage variation in shareholder

wealth (MVA), as explained by each particular explanatory variable.


164

Table 5.12: Results of Relative and Incremental Information Content Tests

Panel A: Results of Relative Information Content Test

PAT > ROCE > EVA > Cp

60.25% 59.40% 34.51% 13.62%

Panel B: Results of Incremental Information Content Test

PAT/EVA PAT/ROCE EVA/ROCE PAT/Cp ROCE/Cp EVA/Cp

(80.27-34.54) (80.00-59.40) (68.8-59.4) (60.26-13.62) (59.37-13.62) (34.51-13.62)

45.73% 20.60% 9.4% 46.64% 45.75% 20.89%

EVA/PAT ROCE/PAT ROCE/EVA Cp/PAT Cp/ROCE Cp/EVA

(80.27-60.25) (80.00-60.25) (68.8-34.54) (60.26-60.25) (59.37-59.40) (34.51-34.54)

20.02% 19.75% 34.26% .01% -0.03 -0.03

Note: The comparison of adjusted R

2 of the first four models in table 5.11 (where each explanatory variable is specified univariately) provides the

results of relative information content test. For the incremental information content test, the adjusted R2 of earlier univariate regressions have

been subtracted from the adjusted R2 of each pair-wise (bivariate) regressions in models V to X in table 5.11.


165

The results in Panel B provide the results of incremental information content tests for the

pair-wise comparisons of all the four explanatory variables. For this purpose, the adjusted

R2 of earlier univariate regressions have been subtracted from the adjusted R

2 of each

pair-wise (bivariate) regressions to identify the incremental information provided by each

explanatory variable in relation to other variables. For instance, in panel B, PAT/EVA

(45.73%) is equal to the information content of the pairwise comparison of PAT and

EVA (80.27%) minus the information content of EVA (34.54%) from table 5.11.

Looking at the pairwise combinations, it can be observed that over the PAT measure

alone, explanatory power has increased by 46.64% and 45.73%. Similarly the

explanatory power has improved by 45.75% and 34.26% respectively over the ROCE

measure alone. Combining both of these measures i.e. PAT and ROCE, the incremental

information content of PAT (20.60%) is slightly more than the incremental information

content of ROCE (19.75%).

As far as the comparison between value based and accounting based measures is

concerned, the results clearly depict that explanatory power improves by 20.89%, 20.02%

and 9.4% respectively over the EVA measure alone. Although it is lesser than the

incremental information provided by the traditional measures PAT and ROCE, yet it

provides the most logical pairing of information variables in explaining MVA i.e. Models

V (that best explains MVA) and XI (all variables considered jointly). Thus individually,

EVA explains as much as 34.54% of the variation in MVA and in combinations, it also

evidences increment information content (although lesser than that of PAT and ROCE).

Thus, the results provide the sufficient evidence that traditional measures of firm

performance (both absolute and relative measures i.e. PAT and ROCE respectively) are

highly associated with its shareholder value creation as measured in terms of MVA.

Finally, the present study denies the hypothesis of equal relative and incremental

information content and identifies that „Earnings‟ dominate EVA (the Value Based

Measure) in explaining the variations in firm value and hence shareholder wealth.

5.8 Potential Factors Contributing to the Failure of EVA to Dominate

Earnings

The present study finds no clear evidence to support Stern & Stewart‟s claim that EVA is

superior to the traditional performance competitors in its association with MVA. On the

contrary, the evidence suggests that the Indian market seems more focused on „Profits‟


166

than value based measure „EVA‟. The study empirically finds that although EVA and

PAT both depict highly positive and significant association with MVA yet PAT‟s

explanatory power is greater than the explanatory power of EVA. Further, the results also

provide the sufficient evidence that traditional measures of firm performance (both

absolute and relative measures i.e. PAT and ROCE respectively) are highly associated

with its shareholder value creation as measured in terms of MVA. That means Indian

market is more responsive to accounting based metrics whether these are expressed in

absolute terms or in relative terms. So, accrual accounting based numbers can

undoubtedly be continued for evaluating corporate financial performance.

As the key findings of the study evidence the Earnings‟ superiority to EVA in relative

information content test (in their association with MVA), the study identifies the potential

factors contributing to the failure of EVA to dominate Earnings in explaining the

variations in shareholder value creation. Kramer and Pushner (1997) explained that with

the market being fed almost constant news on earnings, it is not surprising that it is not

much responsive to EVA in the short-run. Another reason might be that accounting

adjustments and estimates of the capital charge given by the proponents may contain

measurement error relative to what the market uses for valuing firms. Biddle et al. (1997)

observes that in attempting to estimate economic profits, adjustments made by Stern &

Stewart may remove accruals that market participants use to infer firm‟s future prospects.

Thus, while computing EVA, the true measure of company‟s economic profitability is

determined but its association with market returns is lost. Moreover, another reason for

the comparatively weak value- relevance of EVA might be the prevalent notion of

„earnings myopia‟. Biddle et al. (1997) viewed that some adopters of EVA feel that they

must still base their external performance on earnings because this is the measure on

which financial analysts continue to focus. As a result, market fails to recognize the

reporting benefits of EVA. However, the present study does not question the

effectiveness of EVA because inspite of non-availability of detailed financial data

required for EVA related computations and non-mandatory disclosure of EVA

Statements in annual reports of Indian Companies, market seems to be quite responsive to

EVA performance of a company. Thus, the findings advocate adoption of EVA for

management compensation, external communication and security analysis and also


167

suggest disclosure of EVA in financial reporting, to align management objectives with

shareholders‟ interests and facilitate value-based performance monitoring (Holler, 2008).

5.9 Conclusion

Analyzing a pooled, time series, cross-sectional data of 100 Indian companies for a

period of twelve years i.e. from 1996 to 2007, this study has attempted to examine

whether the value based measures of firms performance are more highly associated with

firm‟s MVA than other long established traditional measures. The results indicate that the

variability in the MVA accounted for by the four final predictors comes out to be 80.27%

(adjusted R2). However the study found no clear evidence to support Stern & Stewart‟s

claim that EVA is superior to the traditional performance competitors in its association

with MVA. The empirical evidence suggests that Indian market seems to be more

focused on „Profits‟ than value based measure „EVA‟. Relative tests show the dominance

of PAT and ROCE over EVA; and incremental tests find that solely accounting based

measures provide considerable and significant additional information, whereas EVA

provides comparatively lower incremental information. Thus, Indian market being less

responsive to EVA than PAT needs more ongoing investigation.

Documents

Chapter 5 Relationship of Economic Value Added and Conventional Performance Measures ...shodhganga.inflibnet.ac.in/bitstream/10603/5127/10/10... · 2015-12-04 · Chapter 5 Relationship